Graph Quantum Walk Transformer Redefines Graph Learning with Quantum Walks
Insider Brief: Graph Transformers use global attention mechanisms to model relationships across all graph nodes, excelling in tasks like node classification, link prediction, and graph classification. Traditional Graph Transformers often overlook graph-specific patterns like topology and local connections, leading to redundant information and insufficient focus on structural details. GQWformer, a Read more…